Machine Learning Engineer - Remote

Overview

Blue Orange Digital is a cloud-based data transformation and predictive analytics development firm with offices in NYC and Washington, DC. From startups to Fortune 500’s, we help companies make sense of their business challenges by applying modern data analytics techniques, visualizations, and AI/ML. Founded by engineers, we love passionate technologists and data analysts. Our startup DNA means everyone on the team makes a direct contribution to the growth of the company.

Our Client’s AI team is bringing together data, tech, and data science to build game-changing products and services to accelerate impact with their customers. We are looking for a Machine Learning (ML) Engineer to join their AI Team and support the development of products that serve the needs of their customers, including those in the financial services.

The team is tackling key questions, like - How to ensure successful adoption, integration, and utilization of our ML/AI solutions by our clients and internal teams? How to increase the rigor and scalability of our products to unlock maximum value for our business?

As a growing team, we embrace a start-up mentality while operating in a large organization. We value speed, agility, and impact – and our results and ways of working are transforming the culture of the larger organizations around us. The candidate will have excellent communication skills to collaborate with technical and non-technical stakeholders effectively.

At Blue Orange, you'll have the opportunity to work on cutting-edge projects, leveraging modern machine-learning and AI techniques to deliver tangible business outcomes and drive innovation in our data-driven solutions.

In Short

• Develop and maintain a scalable machine learning framework for data science products
• Work closely with our data scientists to pilot our products with beta customers
• Collaborate with cross-functional teams to understand business requirements and design Gen AI solutions that address complex challenges
• Evaluate and select appropriate algorithms and methodologies to solve specific problems and optimize performance
• Lead the refinement and iteration of prompt engineering techniques to enhance the effectiveness and efficiency of Gen AI systems
• Experiment with retrieval-augmented generation (RAG) approaches to improve the relevance and coherence of AI-generated outputs
• Work closely with software engineers and AI researchers to integrate prompt engineering and RAG components into production systems and ensure seamless deployment

Requirements

• Experience with common data science algorithms and processes (e.g., xgboost, hyperparameter tuning)
• Advanced knowledge in Python/PySpark and SQL
• Experience working with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, GCP)
• Experience with prompt engineering techniques and experience in refining and iterating on prompts for AI models
• Knowledge of retrieval-augmented generation (RAG) methodologies and experience in implementing RAG-based approaches for text generation tasks
• Interest or previous experience in financial services and/or credit modeling
• Excellent problem-solving skills, critical thinking abilities, and attention to detail
• Effective communication skills and the ability to collaborate effectively in a team environment
• 3+ years of industry experience as a hands-on practitioner of machine learning engineering
• Proven experience with Gen AI systems
• Bachelor’s degree in computer science, engineering, or a related field
• Preferred qualifications:
• Advanced degree in a relevant field.
• Publications in relevant AI/ML communities and journals.
• Deep Learning Expertise Tensorflow and/or Pytorch
• Experience Fine-tuning OpenSource LLMs and deploying them.
• Great Expectations and/or DBT is a plus.

Benefits

• Fully remote
• Flexible Schedule
• Unlimited Paid Time Off (PTO)
• Paid parental/bereavement leave
• Worldwide recognized clients to build skills for an excellent resume
• Top-notch team to learn and grow with